In this text, unwanted sociological bias encompasses several forms of discrimination that may manifest in ML, including overt discrimination, disparate treatment, and disparate impact (DI), i.e., unintentional discrimination. DI may be caused by model misspecification, inaccurate or incomplete data, or data that has differing correlations or dependencies among demographic groups of individuals, driving differences in favorable model outcomes. A model is said to be biased here if, (1) group membership is not independent of the likelihood of a favorable outcome, or (2) under certain circumstances, membership in a subset of a group is not independent of the likelihood of a favorable outcome (i.e., local bias). Underlying discrimination that causes bias may or may not be illegal, depending on how it arises and applicable discrimination laws. Herein fairness techniques are used to diagnose and remediate unwanted sociological bias in ML models. Diagnosis approaches include DI testing and other tests for bias [1]}. Remediation methods tend to involve model selection by minimization of bias, preprocessing training data, e.g., reweighing [2]}, training unbiased models, e.g., adversarial de-biasing [3]}, or post-processing model predictions, e.g., by equalized odds [4]}.And similar methods, e.g.: http://www.fatml.org/resources/relevant-scholarship.
